The Forex Trading Course. Cofnas Abe

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Название The Forex Trading Course
Автор произведения Cofnas Abe
Жанр Зарубежная образовательная литература
Серия
Издательство Зарубежная образовательная литература
Год выпуска 0
isbn 9781118998687



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is challenging central banks and monetary policy. The result is an environment of constant focus on economic data and therefore more potential for surprises. It is an exciting time for trading. In the next chapter, we take a closer look at inflation.

      Since it is likely that the world is entering an era where interest rate decreases are essentially over, the forex trader should understand the general impact and role of interest rate increase. Interest rate increases do much more than slow down an economy; they also act as a magnet to attract capital to bonds and other interest-bearing instruments. This has been called an appetite for yield, and when applied globally the flow of capital in and out of a country can be substantially affected by the difference in interest rates between one country and another. In the coming years, if interest rate increases are not uniform around the world, the phenomenon of the “carry trade” will likely come back into focus. The carry trade is driven by the interest rate differential that exists between currencies – for example, between Japan (0.10 %) and New Zealand (3.5 %), causing low-cost borrowing in yen to invest in higher-yielding kiwis. Historically the yen was the low-interest-rate currency that was borrowed or sold to finance investments in instruments with higher interest rates. It can lead to market turbulence, however.

Some forex traders learned this lesson, about the consequences of carry trades, when the US stock market sold off on February 27, 2007. It was precipitated by traders getting out of their carry trade positions. Since billions of dollars were sold to be converted back into yen, equity markets were also affected because equity positions had to be sold to buy back the yen positions. In Figure 1.1 we see how the Dow Jones Industrial Index correlated directly with the US dollar–Japanese yen (USDJPY) pair that day. Many traders will find the USDJPY relationship to the US equities highly correlated. When the markets are risk averse, the yen strengthens against the dollar. When the markets are risk-on, the yen weakens. Figure 1.2 shows how the USDJPY weakened while the Dow Jones Industrial Index strengthened.

Figure 1.1 Dollar-to-Yen Slide Causes Dow Sell-Off.

      Source: CQG Inc., Copyright © 2006. All rights reserved worldwide.

Figure 1.2 Dollar-to-Yen Surges in Tandem with Dow Jones Uptrend.

      The fundamental fact that interest rates will increase or decrease at different times around the world will create trading opportunities not seen in a decade or more.

The Role of Housing Data in Forex Price Movements

      Fundamentally, however, one of the most important categories of economic data around the world, which is sensitive to interest rate changes, is housing data. The housing sector in the United States, as well as other nations, provides a major share of wealth, consumer spending, and job creation. Expectations about rates of growth or decline in housing data are important indicators to watch. Of particular importance is to watch for housing bubbles. Before the collapse of 2008, there was an international housing boom, with prices growing at more than 10 percent per year in many countries. For example, Ireland grew at 15 percent in 2006; Spain's growth actually slowed to 13 percent. Canada, Norway, and Sweden shared more than 10 percent growth. The United States saw prices up 7 percent. The increased wealth fueled economic growth and consumer purchasing.

      Closely watched are data releases that relate to housing activity. Some of the main data releases track the following:

      • Unsold homes

      • Mortgage loan applications

      • New and existing home sales

      • Single-family housing permits

      • Housing prices

      Forex traders' expectations of the future direction of interest rates are significantly affected by housing data because, for example, weak housing leads to expectations of a slowdown on consumption. The economic reasoning is that consumers start seeing a decline in housing values and restrain their consumer spending. The collapse of the housing boom in 2008 was a global phenomenon. Housing recovery in the United States and Great Britain are and will be highlights of the strength of the overall recovery.

      One of the most important indicators in periods of housing growth before the financial collapse of 2008 was the level of mortgage equity withdrawals (MEWs). As home prices increased around the world, consumers took out loans against their mortgages, which stimulated consumption. During periods of housing booms, MEWs rise. MEWs have been, in fact, calculated to contribute to the growth of gross domestic product (GDP). However, if MEWs slow down, or remain low, this can portend a decline in consumption and a slowdown in the economy. If and when a slowdown in MEWs occurs, the impact is that of lessening the likelihood of an interest rate increase. Damon Darlin wrote in the New York Times:

      Economists argue over what effect the access to money, which mortgage equity withdrawals allow, has had on consumer spending. Homeowners cash out to pay off more expensive credit card debt, remodel the house to build more equity, or just have fun. They may very well have used it to buy another house or not spent it at all, but added it to savings. Economists really are not certain.

      “I guess it is one of those mysteries,” said Christopher D. Carroll, an economics professor at Johns Hopkins University. “I don't think anyone knows what the answer is.”

      Nevertheless, mortgage equity withdrawal is closely watched as an indicator of the general economy because, Mr. Carroll said, “there is a lot of concern that a cooling housing market could result in a sharp fallback in consumer spending.”

      A recent paper that Mr. Carroll helped write contends that for every $1,000 change in housing wealth there is an immediate propensity to consume about $20 more. The wealth effect, as the phenomenon is called, is twice as high for housing wealth as it is for stock wealth, Mr. Carroll and his associates said. 2

      The 2008 financial collapse generated a decline of housing values. Consequently, MEWs declined because home to equity values declined, eliminating the ability to loan against it. The issuance of subprime mortgages created housing stock that had very high loan/home ratios and encouraged the collapse when values declined and homeowners couldn't keep up the payments. Economic forces ultimately worked to create mortgage delinquencies and a collapse in this market. In Great Britain, the Bank of England no longer publishes MEWs as a separate data series. But forex traders should keep an eye on them if an uptrend is spotted.

      For the forex trader, it is a clear case where housing fundamentals affect the dollar. More housing strength translates to greater consumer demand, and that translates to raising the probability of interest rate increases by the central bank. It's difficult to be bullish on the currency whose economy is not strong in the housing sector.

      

Assignment

      Find the recent MEW rates of the past quarter in Canada, Australia, and the United Kingdom. This will take some exploration on the Internet, but it is worth tracking.

Housing Data as a Leading Indicator

      What is important to realize about fundamental analysis of housing sector data is that the trader can use the data to identify pending changes in trends and direction of the economy. Of course, it is true that forex prices move all the time in reaction to news (or rumors), but economies don't change direction overnight. By understanding housing data, one can develop a fundamental viewpoint that leads to deciding on being bullish or bearish on the currency involved before technical price patterns reflect the underlying change.

For example, in Table 1.1 we see data on US new housing starts during the pre-collapse era. The year 2005 was a year of a high level of housing starts, peaking in February at 2.2 million units and then testing that peak in January 2006 (see Figure 1.2). After January 2006, the data showed a decline, and by August 2006, the decline in housing